access-meteorological-data-era5-merra2

Access meteorological data from MERRA2 and ERA5 in one line¶

This notebook demonstrates how to use the PVRADAR Python SDK to access historical weather data from multiple sources — including MERRA2 and ERA5 — with minimal code. We'll retrieve and visualize key environmental variables such as temperature, rainfall, and irradiance.

In [1]:

from pvradar.sdk import PvradarSite, R, resource_plot, describe

Define location and time period¶

In [2]:

# Define site and time period for the simulation
location = (35.0545, -106.5390)  # SANDIA Labs, Albuquerque, NM, USA
site = PvradarSite(location=location, interval='2020-01-01..2020-12-31')

Retrieve air temperature data¶

We'll retrieve hourly air temperature data from three different sources: merra2, era5-global, and era5-land. Use R-Notation, PVRADAR's declarative way of requesting time-series inputs to define the datasource.

For more on R-Notation and available attributes, see the Resource DB and R-Notation Guide.

In [3]:

# Retrieve air temperature from MERRA2
air_temp_merra2 = site.resource(R.air_temperature(datasource='merra2'))

# Retrieve air temperature from two different ERA5 datasets
air_temp_era5global = site.resource(R.air_temperature(datasource='era5', dataset='era5-global'))
air_temp_era5land = site.resource(R.air_temperature(datasource='era5', dataset='era5-land'))

Resources are just ordinary pandas Series that carry additional meta data. Use our describe function to investigate.

In [4]:

describe(air_temp_merra2)

Out[4]:

air_temperature: mean hourly air temperature in degC from merra2
8784 data points (2020-01-01 00:00:00-07:00 to 2020-12-31 23:00:00-07:00)

Visualize air temperature from different sources¶

In [5]:

# Plot temperature series from different sources
resource_plot(air_temp_era5global, air_temp_era5land, air_temp_merra2)

No description has been provided for this image

Compare rainfall data¶

Rainfall is typically a cumulative variable. We'll compare daily rainfall data from ERA5 and MERRA2, and request the data in centimeters (cm).

In [6]:

# Daily rainfall from ERA5 (default is 'era5-land' dataset)
rainfall_era5 = site.resource(R.rainfall(datasource='era5', to_unit='cm', to_freq='D'))

# Daily rainfall from MERRA2
rainfall_merra2 = site.resource(R.rainfall(datasource='merra2', to_unit='cm', to_freq='D'))

Visualize rainfall comparison¶

In [7]:

# Plot daily rainfall comparison between sources
resource_plot(rainfall_era5, rainfall_merra2)

Retrieve and visualize irradiance¶

We'll now retrieve and visualize global horizontal irradiance (GHI), a key metric for solar energy applications.

In [8]:

# Retrieve global horizontal irradiance
ghi = site.resource(R.global_horizontal_irradiance)

# Plot GHI
resource_plot(ghi)

Overlay all key environmental variables¶

In [9]:

resource_plot(
    air_temp_era5global,
    air_temp_era5land,
    air_temp_merra2,
    rainfall_era5,
    rainfall_merra2,
    ghi,
)

Summary¶

In just a few lines of code, we accessed and compared meteorological data from multiple satellite sources — including ERA5 and MERRA2. PVRADAR automatically handles unit conversions, frequency resampling, and data alignment, enabling quick and consistent analysis.

¶

Why PVRADAR?¶

Model and optimize the real-world behavior of your PV plants¶

PVRADAR allows you to model the effect of any technical or environmental factor on plant performance:

Combine snow, soiling, albedo, clipping, battery storage, and more in a single modeling workflow.
Quantify impacts in terms of energy yield, PR, LCOE, and financial KPIs.
Explore what-if scenarios and optimize plant design and O&M strategies.

Build internal tools and advanced applications¶

With PVRADAR, you can turn your models into robust, organization-wide tools:

Build internal web apps that allow colleagues to apply validated models without writing code.
Standardize modeling across teams and projects.
Automate reporting and performance analysis to save time and ensure consistency.

One Python line away from your data¶

The PVRADAR SDK simplifies data access:

Seamlessly connect to satellite data, meteo stations, and internal performance databases.
Standardize and pre-process data for modeling — ready to use in one line of Python.
Keep full control over your data, whether in the cloud or on-premises.

Supported by deep expertise¶

PVRADAR is more than software — it is backed by industry-leading expertise:

Model validation and tuning with high-quality reference data.
Consulting and support on snow losses, soiling, O&M optimization, and yield forecasting.
Proven track record with leading IPPs, developers, and consultants.

👉 Ready to go further? Contact us to learn how we can help solve your modeling challenge!